Experimental validation of optimal real-time energy management system for microgrids

A real-time evaluation of energy management systems for smart hybrid home Microgrids

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

The revolution of microgrids (MGs) in the modern world has impacted the power systems to deal with the loads with optimal use of renewable energy resources (RERs). In the models consisting of energy management systems (EMS) and generation sources, there will be power fluctuations between the source and the load under certain conditions due to the unpredictable behavior of RERs. Therefore, a reliable scheme has to be implemented between the source and the load to ensure optimal performance of direct current (DC)‐MGs. This article has presented a review of the EMS models introduced by various researchers to enable optimal power supply and efficient operation of the DC‐MGs. A thorough illustration of the schemes adopted to provide efficient operations under grid‐connected and islanded mode of operations are provided. The effects and advantages of centralized and decentralized EMSs are discussed. The role of a battery energy storage system (BESS) in microgrid scheduling is presented and compared with a few other energy storage systems. The scheduling strategies implemented for optimal EMS using different algorithms and techniques are elaborated. In addition, the effects of battery degradation and the formulated solutions to improve the degraded models with improved battery life are also provided with cycle life and calendar life. Then, the lithium ion and lead acid BESS effect on microgrid EMS is presented. Finally, the energy management methods for DC‐MGs end with challenges, research gaps, and future direction.

This dissertation proposes an elite converter for the optimal utilization of hybrid renewable energy sources in the microgrid-associated systems using hybrid technique. Microgrid (MG)-associated systems are photovoltaic, wind turbine and battery. The proposed elite converter is the high conversion ratio (HCR) DC–DC converter with a maximum power point tracking (MPPT) controller. The main advantage of the converter is to eliminate the problem of high switch voltage stress. The proposed hybrid technique is the joint execution of both the adaptive whale optimization algorithm (AWOA) and adaptive neuro-fuzzy interference system (ANFIS), and hence, it is named as AWO-ANFIS technique. Here, AWOA technique is utilized to establish the exact control signals based on the power variation between the source side (renewable energy sources and battery) and load side. ANFIS technique is utilized for error minimization by using the achieved dataset from AWOA technique. Here, the reference currents of the controller are managed in view of the MPPT for solar and wind energy. In the proposed technique, the objective function is defined by the system data subject to equality and inequality constraints. Finally, the proposed technique is executed in the MATLAB/Simulink working platform and the execution is compared with the existing techniques. To test the performance of the proposed method, different case studies are conducted such as variation of irradiance under constant wind speed, variation of PV power under constant irradiance and load variation under constant irradiance and wind speed. For all the cases, the response of current, voltage and power of all the sources is analyzed and compared with that of the existing techniques such as GA, PSO and WOA. The statistical analysis of the proposed technique is also analyzed. Likewise, the computation time using various trails such as 100, 250, 500 and 1000 is also analyzed. The comparison results affirm that the proposed technique has less computational time than the exiting techniques. Moreover, the proposed method is cost-effective power production of MG and effective utilization of renewable energy source without wasting the available energy.

Immense growth has happened in the field of microgrid (MG) and the energy management system (EMS) methods in the past decade. It is estimated that there is still a huge potential of growth remaining in the field of EMS in the coming years. The main role of EMS is to autonomously determine hour-by-hour the optimum dispatch of MG and main grid energy to satisfy load demand needs. This paper is focused on developing an advanced EMS model able to determine the optimal operating strategies regarding to energy costs minimization, pollutant emissions reduction, MG system constraints and better utilization of renewable resources of energy such as wind and photovoltaic through daily load demand. The proposed optimization model of EMS is formulated and solved based on genetic algorithm (GA). The efficient performance of the algorithm and its behavior is illustrated and analyzed in detail considering winter load demand profile.

Impact of energy storage system and distributed energy resources on bidding strategy of micro-grid in deregulated environment

A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed energy sources and loads capable of operating in parallel with or independently from the main power grid. The microgrid concept integrated with renewable energy generation and energy storage systems has gained significant interest recently, triggered by increasing demand for clean, efficient, secure, reliable and sustainable heat and electricity. However, the concept of efficient integration of energy storage systems faces many challenges (e.g., charging, discharging, safety, size, cost, reliability and overall management). Additionally, proper implementation and justification of these technologies in MGs cannot be done without energy management systems, which control various aspects of power management and operation of energy storage systems in microgrids. This review discusses different energy storage technologies that can have high penetration and integration in microgrids. Moreover, their working operations and characteristics are discussed. An overview of the controls of energy management systems for microgrids with distributed energy storage systems is also included in the scope of this review.

Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading

Accelerating decarbonization commitments are driving the deployment of renewable-dominant microgrids (MG) to extend reliable, affordable, and clean electricity to remote areas. In power systems with high renewable energy sources penetration, variability in photovoltaic generation (PV) makes energy storage a pivotal resource; this role becomes particularly critical in islanded operation, where its lifetime economics hinge on how battery degradation is modeled inside the Energy Management System (EMS). Second-life batteries (SLB) offer a promising, lower-cost, circular-economy option for stationary storage; however, their heterogeneous aging histories complicate Remaining Useful Life (RUL) estimation and operational planning. This paper presents the development and theoretical implementation of a degradation model for an SLB pack in an isolated MG EMS. The system comprised a PV system, diesel generator, and SLB. An empirical degradation model of SLBs was incorporated into the EMS objective function and compared with a linear model. The empirical model was refined using two additional factors: one to represent thermal heterogeneity at the module level and the other to capture calendar aging and the effect of the average state of charge. The results show that the choice of degradation model directly impacts the costs, dispatch strategy, and estimation of the remaining useful life. Furthermore, it was determined that temperature and internal thermal gradients are the most decisive external factors in the degradation of stationary systems. Finally, it was concluded that SLBs can operate between 6.9 and 11.3 years (equivalent to 2520–4114 full cycles), depending on the severity of the operating conditions and model parameterization. • Energy management system for microgrids with second-life batteries (SLB). • Economic dispatch formulation with degradation model of SLB. • Technical and economic viability of SBL applied to microgrids. • Thermal heterogeneity, average state of charge, and aging calendar improved degradation model. • Temperature and intra-module thermal gradients relevant aspects for SLB.

Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.

Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles

microgrids (MGs) are a promising solution to global energy challenges. Robust and reliable operation while maximizing infeed from renewable sources and reducing the use of thermal generation are important objectives in the control of MGs. In this paper, two model predictive control strategies are applied to operate a grid-connected MG in the presence of bounded uncertainties in an optimal way. One is a certainty-equivalence and the other a minimax approach. They differ in the disturbance model used for renewable energy source infeed and load. To find an optimal operation strategy, a mixed-integer quadratic program is solved online. In a numerical case study, the results of two approaches are analyzed. The simulation shows, that compared to the performance of the certainty-equivalent case, the minimax approach is robust to disturbances and allows a secure operation of MGs in grid-connected mode.

Regarding the advances in technology and anxieties around high and growing prices of fossil fuels, government incentives increase to produce cleaner and sustainable energy through distributed generations. This makes trends in the using microgrids which consist of electric demands and different distributed generations and energy storage systems. The optimum operation of microgrids with considering demand-side management increases efficiency and reliability and maximize the advantages of using distributed generations. In this paper, the optimal operation scheduling and unit commitment of generation units installed in a microgrid are investigated. The microgrid consists of technologies based on natural gas that are microturbine and phosphoric acid fuel cell and technologies based on renewable energy, including wind turbine and photovoltaic unit along with battery energy storage system and plug-in electric vehicle commercial parking lot. The goal of the paper is to solve a multi-objective problem of maximizing revenues of microgrid operator and minimizing emissions. This paper uses an augmented epsilon constraint method for solving the multi-objective problem in a stochastic framework and also implements a fuzzy-based decision-maker for choosing the suitable optimal solution amid Pareto front solutions. This new model implements the three type of the price-based and incentive-based demand response program. It also considers the generation reserve in order to enhance the flexibility of operations. The presented model is tested on a microgrid and the results demonstrate the efficacy of the proposed model economically and environmentally compared to other methods.

Renewable energy sources are widely acknowledged as the optimal substitutes for conventional energy outlets globally. Countries are endowed with diverse renewable resources, including solar, wind, biomass, hydro, and tidal energy. Despite this abundance, there exists a substantial disparity between the demand and supply of electrical energy, with numerous regions still facing insufficient access to power. The integration of various renewable energy sources in remote and isolated locations forms a Microgrid (MG), catering adequately to local energy requirements. These microgrids have the capability to function seamlessly alongside conventional grids. Hybrid MG system, incorporating Photovoltaic (PV) with battery storage and a Wind Turbine (WT), emerges as a practical solution for electrifying remote areas in islanded mode. The WT’s installed capacity is chosen to meet critical load requirements during periods of non-availability of renewable sources. The battery's capacity is selected to cover the transition period between renewable sources or as a backup source, ensuring cost-effectiveness over WTs. The application of Adaptive Particle Swarm Optimization (APSO) has demonstrated favorable outcomes compared to existing PSO algorithms, enhancing control efficiency. In a grid-connected MG system, the integration of distributed PV generation has notably improved the voltage profile, reducing overall losses. Consequently, the presented MG systems provide both technically and economically viable solutions for ensuringcontinuous electricity supply in isolated and grid-connected systems. This approach not only mitigates pollution but also allows for the possibility of capacity addition, fostering sustainable growth in these regions.

Microgrid (MG) is basically composed of different distribution generators (DGs) connected in parallel for supplying a specific set of loads managed by an Energy management system (EMS). EMS is a control system integrated within MGs for managing the operations of these DGs effectively to fulfill a power balance between power production and load demand in the most optimal way, especially in island MGs. In this paper, an EMS based on Multiple Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (MANFIS-GA) is proposed for PV/Wind/Diesel Generator/Battery (PWDB) island MG system, to optimize the output power of diesel generator, manage charging-discharging operation of MG Battery Storage keeping its State of Charge (SOC) in acceptable limits, and improve the MG system reliability and stability by mitigating the effects of sudden changes in the electrical loading and Renewable energy sources (RES) Power. The prediction system is implemented by using 8760 samples based on an hourly meteorological data of a whole year. GA is used as an optimization technique for training MANFIS to accomplish the desired objects of EMS. For evaluation purpose, a real case study of a day-ahead data is tested and discussed in details. Experiments show that the proposed smart system provides accurate results for the expected outputs and achieves a good performance.